A method for building efficient frameworks by directly imitating two-stage features

Abstract

The present invention provides a method for building an efficient framework by directly imitating two-stage features, including: S1, constructing a modeled feature pyramid network backbone network with resnet101 and FPN network; S2, after extracting features in FPN, using the Refinement module to filter out negative effects , adjust the position and size of the predefined anchor box; S3, the branch of the two-stage detection head, detect the sparse set of anchor boxes adjusted by the Refinement module, and divide T-head into two branches for classification and regression; S4, the one-stage detection head branch, design it as a lightweight network; S5, define the training loss function, improve the accuracy of the first-stage detector, make it easier for the first-stage detector to obtain useful information, and make it easier to obtain useful information without increasing the calculation cost Under this condition, the high precision of the two-stage detection head and the high efficiency of the one-stage detection head can be obtained.

Description

technical field [0001] The invention relates to the field of deep learning computer vision, in particular to a method for building an efficient framework by directly imitating two-stage features. Background technique [0002] Existing object detection methods can be divided into one-stage methods and two-stage methods. One-stage detectors are more efficient due to their simple architecture, while two-stage detectors lead in terms of accuracy due to their structures that generate more accurate candidate boxes. Although recent works try to improve one-stage detectors by imitating the structural design of two-stage detectors, their accuracy gap is still large. We propose a novel and efficient framework for training one-stage detectors by directly imitating two-stage features, aiming to bridge the accuracy gap between one-stage and two-stage detectors. Different from traditional analog methods, this method has a shared backbone for one-stage and two-stage detectors, which is t...

AI Technical Summary

Problems solved by technology

Although recent work attempts to improve one-stage detectors by imitating the structural design of two-stage detectors, the accuracy gap is still large

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